{"id":324541,"date":"2023-02-17T04:00:00","date_gmt":"2023-02-17T03:00:00","guid":{"rendered":"https:\/\/blog.jetbrains.com\/?post_type=pycharm&p=324541"},"modified":"2023-02-16T10:15:44","modified_gmt":"2023-02-16T09:15:44","slug":"using-pycharm-to-read-data-from-a-mysql-database-into-pandas","status":"publish","type":"pycharm","link":"https:\/\/blog.jetbrains.com\/zh-hans\/pycharm\/2023\/02\/using-pycharm-to-read-data-from-a-mysql-database-into-pandas\/","title":{"rendered":"\u4f7f\u7528 PyCharm \u5c06 MySQL \u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u8bfb\u5165 pandas"},"content":{"rendered":"

\u5728\u6570\u636e\u79d1\u5b66\u4e4b\u65c5\u4e2d\uff0c\u60a8\u8fdf\u65e9\u4f1a\u9047\u5230\u9700\u8981\u4ece\u6570\u636e\u5e93\u4e2d\u83b7\u53d6\u6570\u636e\u7684\u60c5\u51b5\u3002 \u7136\u800c\uff0c\u4ece\u5c06\u672c\u5730\u5b58\u50a8\u7684 CSV \u6587\u4ef6\u8bfb\u5165 pandas \u5230\u8fde\u63a5\u548c\u67e5\u8be2\u6570\u636e\u5e93\uff0c\u8fd9\u53ef\u80fd\u662f\u4e00\u9879\u8270\u5de8\u7684\u4efb\u52a1\u3002 \u5728\u4e00\u7cfb\u5217\u535a\u6587\u7684\u7b2c\u4e00\u7bc7\u4e2d\uff0c\u6211\u4eec\u4f1a\u63a2\u8ba8\u5982\u4f55\u5c06\u5b58\u50a8\u5728 MySQL \u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u8bfb\u5165 pandas\uff0c\u5e76\u67e5\u770b PyCharm \u4e2d\u4f7f\u8fd9\u9879\u4efb\u52a1\u66f4\u7b80\u5355\u7684\u4e00\u4e9b\u529f\u80fd\u3002<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

\u67e5\u770b\u6570\u636e\u5e93\u5185\u5bb9<\/h2>\n

\u5728\u672c\u6559\u7a0b\u4e2d\uff0c\u6211\u4eec\u4f1a\u5c06\u4e00\u4e9b\u5173\u4e8e\u822a\u7a7a\u516c\u53f8\u5ef6\u8bef\u548c\u53d6\u6d88\u7684\u6570\u636e\u4ece MySQL \u6570\u636e\u5e93\u8bfb\u53d6\u5230 pandas DataFrame \u4e2d\u3002 \u8fd9\u4e9b\u6570\u636e\u662f Airline Delays from 2003-2016<\/a> \u6570\u636e\u96c6\u7684\u4e00\u4e2a\u7248\u672c\uff0c\u8be5\u6570\u636e\u96c6\u7531 Priank Ravichandar<\/a> \u63d0\u4f9b\uff0c\u4ee5 CC0 1.0<\/a> \u6388\u6743\u3002<\/p>\n

\u4f7f\u7528\u6570\u636e\u5e93\u53ef\u80fd\u4ee4\u4eba\u611f\u5230\u56f0\u60d1\u7684\u7b2c\u4e00\u4ef6\u4e8b\u662f\u65e0\u6cd5\u7b80\u8981\u4e86\u89e3\u53ef\u7528\u6570\u636e\uff0c\u56e0\u4e3a\u6240\u6709\u8868\u90fd\u5b58\u50a8\u5728\u8fdc\u7a0b\u670d\u52a1\u5668\u4e0a\u3002 \u56e0\u6b64\uff0c\u6211\u4eec\u8981\u4f7f\u7528\u7684\u7b2c\u4e00\u4e2a PyCharm \u529f\u80fd\u662f Database<\/em>\uff08\u6570\u636e\u5e93\uff09\u5de5\u5177\u7a97\u53e3\uff0c\u5b83\u5141\u8bb8\u60a8\u5728\u8fdb\u884c\u4efb\u4f55\u67e5\u8be2\u4e4b\u524d\u8fde\u63a5\u5230\u6570\u636e\u5e93\u5e76\u5bf9\u5176\u8fdb\u884c\u5168\u9762\u5185\u7701\u3002<\/p>\n

\u4e3a\u4e86\u8fde\u63a5\u5230\u6211\u4eec\u7684 MySQL \u6570\u636e\u5e93\uff0c\u6211\u4eec\u9996\u5148\u8981\u5bfc\u822a\u5230 PyCharm \u7684\u53f3\u4fa7\uff0c\u7136\u540e\u70b9\u51fb Database<\/em>\uff08\u6570\u636e\u5e93\uff09\u5de5\u5177\u7a97\u53e3\u3002<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

\u5728\u8fd9\u4e2a\u7a97\u53e3\u7684\u5de6\u4e0a\u65b9\uff0c\u60a8\u4f1a\u770b\u5230\u4e00\u4e2a\u52a0\u53f7 (+) \u6309\u94ae\u3002 \u70b9\u51fb\u8fd9\u4e2a\u6309\u94ae\u5c31\u4f1a\u51fa\u73b0\u4e0b\u9762\u7684\u4e0b\u62c9\u5bf9\u8bdd\u7a97\u53e3\uff0c\u6211\u4eec\u5c06\u5728\u5176\u4e2d\u9009\u62e9 Data Source <\/em>| MySQL<\/em>\uff08\u6570\u636e\u6e90 | MySQL\uff09\u3002<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

\u6211\u4eec\u73b0\u5728\u6709\u4e00\u4e2a\u5f39\u51fa\u7a97\u53e3\uff0c\u5b83\u5c06\u5141\u8bb8\u6211\u4eec\u8fde\u63a5\u5230\u6211\u4eec\u7684 MySQL \u6570\u636e\u5e93\u3002 \u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u4f7f\u7528\u7684\u662f\u672c\u5730\u6258\u7ba1\u7684\u6570\u636e\u5e93\uff0c\u56e0\u6b64\uff0c\u6211\u4eec\u5c06 Host<\/em>\uff08\u4e3b\u673a\uff09\u8bbe\u4e3a\u201clocalhost\u201d\uff0c\u5c06 Port<\/em>\uff08\u7aef\u53e3\uff09\u8bbe\u4e3a\u9ed8\u8ba4\u7684 MySQL \u7aef\u53e3\u201c3306\u201d\u3002 \u6211\u4eec\u5c06\u4f7f\u7528\u201c\u7528\u6237\u548c\u5bc6\u7801\u201d\u4f5c\u4e3a Authentication<\/em>\uff08\u8eab\u4efd\u9a8c\u8bc1\uff09\u9009\u9879\uff0c\u5e76\u5728 User<\/em>\uff08\u7528\u6237\uff09\u548c Password<\/em>\uff08\u5bc6\u7801\uff09\u4e2d\u8f93\u5165\u201cpycharm\u201d\u3002 \u6700\u540e\uff0c\u5728 Database<\/em>\uff08\u6570\u636e\u5e93\uff09\u4e2d\u8f93\u5165\u6211\u4eec\u7684\u6570\u636e\u5e93\u540d\u79f0\u201cdemo\u201d\u3002 \u5f53\u7136\uff0c\u8981\u8fde\u63a5\u5230\u60a8\u81ea\u5df1\u7684 MySQL \u6570\u636e\u5e93\uff0c\u60a8\u5c06\u9700\u8981\u7279\u5b9a\u4e3b\u673a\u3001\u6570\u636e\u5e93\u540d\u79f0\uff0c\u4ee5\u53ca\u60a8\u7684\u7528\u6237\u540d\u548c\u5bc6\u7801\u3002 \u8bf7\u53c2\u9605\u6587\u6863<\/a>\u4e86\u89e3\u5168\u90e8\u9009\u9879\u3002<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

\u63a5\u4e0b\u6765\uff0c\u70b9\u51fb Test Connection<\/em>\uff08\u6d4b\u8bd5\u8fde\u63a5\uff09\u3002 PyCharm \u63d0\u793a\u6211\u4eec\u6ca1\u6709\u5b89\u88c5\u9a71\u52a8\u7a0b\u5e8f\u6587\u4ef6\u3002 \u7ee7\u7eed\uff0c\u70b9\u51fb Download Driver Files<\/em>\uff08\u4e0b\u8f7d\u9a71\u52a8\u7a0b\u5e8f\u6587\u4ef6\uff09\u3002 Database<\/em>\uff08\u6570\u636e\u5e93\uff09\u5de5\u5177\u7a97\u53e3\u4e00\u4e2a\u975e\u5e38\u597d\u7684\u529f\u80fd\u662f\uff0c\u5b83\u4f1a\u81ea\u52a8\u4e3a\u6211\u4eec\u627e\u5230\u5e76\u5b89\u88c5\u6b63\u786e\u7684\u9a71\u52a8\u7a0b\u5e8f\u3002<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

\u6210\u529f\uff01 \u6211\u4eec\u5df2\u7ecf\u8fde\u63a5\u5230\u6211\u4eec\u7684\u6570\u636e\u5e93\u3002 \u6211\u4eec\u73b0\u5728\u53ef\u4ee5\u5bfc\u822a\u5230 Schemas<\/em>\uff08\u67b6\u6784\uff09\u9009\u9879\u5361\uff0c\u5e76\u9009\u62e9\u6211\u4eec\u60f3\u8981\u5185\u7701\u7684\u67b6\u6784\u3002 \u5728\u6211\u4eec\u7684\u793a\u4f8b\u6570\u636e\u5e93\u4e2d\uff0c\u6211\u4eec\u53ea\u6709\u4e00\u4e2a\uff08\u201cdemo\u201d\uff09\uff0c\u4f46\u5728\u60a8\u6709\u975e\u5e38\u5927\u7684\u6570\u636e\u5e93\u7684\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u901a\u8fc7\u53ea\u5185\u7701\u76f8\u5173\u6570\u636e\u5e93\u6765\u8282\u7701\u65f6\u95f4\u3002<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

\u6240\u6709\u8fd9\u4e9b\u90fd\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u8fde\u63a5\u5230\u6211\u4eec\u7684\u6570\u636e\u5e93\u4e86\u3002 \u70b9\u51fb OK<\/em>\uff08\u786e\u5b9a\uff09\uff0c\u7136\u540e\u7b49\u5f85\u51e0\u79d2\u949f\u3002 \u73b0\u5728\u53ef\u4ee5\u770b\u5230\uff0c\u6211\u4eec\u7684\u6574\u4e2a\u6570\u636e\u5e93\u5df2\u88ab\u5185\u7701\uff0c\u76f4\u5230\u8868\u5b57\u6bb5\u7ea7\u522b\u53ca\u5176\u7c7b\u578b\u3002 \u8fd9\u4f7f\u6211\u4eec\u5728\u8fd0\u884c\u67e5\u8be2\u4e4b\u524d\u5c31\u80fd\u5bf9\u6570\u636e\u5e93\u4e2d\u7684\u5185\u5bb9\u6709\u4e00\u4e2a\u5f88\u597d\u7684\u4e86\u89e3\u3002<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

\u4f7f\u7528 MySQL Connector \u8bfb\u5165\u6570\u636e<\/h2>\n

\u73b0\u5728\uff0c\u6211\u4eec\u5df2\u7ecf\u77e5\u9053\u6570\u636e\u5e93\u4e2d\u7684\u5185\u5bb9\uff0c\u53ef\u4ee5\u51c6\u5907\u8fdb\u884c\u67e5\u8be2\u4e86\u3002 \u5047\u8bbe\u6211\u4eec\u60f3\u8981\u67e5\u770b 2016 \u5e74\u81f3\u5c11\u6709 500 \u6b21\u5ef6\u8bef\u7684\u673a\u573a\u3002 \u901a\u8fc7\u67e5\u770b\u5185\u7701\u7684 airlines<\/code> \u8868\u4e2d\u7684\u5b57\u6bb5\uff0c\u6211\u4eec\u770b\u5230\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u67e5\u8be2\u83b7\u5f97\u8fd9\u4e9b\u6570\u636e\uff1a<\/p>\n

SELECT AirportCode,\n      SUM(FlightsDelayed) AS TotalDelayed\n   FROM airlines\n   WHERE TimeYear = 2016\n   GROUP BY AirportCode\n   HAVING SUM(FlightsDelayed) > 500;<\/pre>\n
\u6211\u4eec\u4f7f\u7528 Python \u8fd0\u884c\u8fd9\u4e2a\u67e5\u8be2\u7684\u7b2c\u4e00\u79cd\u65b9\u5f0f\u662f\u4f7f\u7528\u4e00\u4e2a\u53eb\u4f5c MySQL Connector \u7684\u8f6f\u4ef6\u5305\uff0c\u5b83\u53ef\u4ee5\u4ece PyPI<\/a> \u6216 Anaconda<\/a> \u5b89\u88c5\u3002 \u5982\u679c\u9700\u8981\u5173\u4e8e\u8bbe\u7f6e pip<\/a> \u6216 conda<\/a> \u73af\u5883\u6216\u5b89\u88c5\u4f9d\u8d56\u9879<\/a>\u7684\u6307\u5bfc\uff0c\u8bf7\u53c2\u9605\u94fe\u63a5\u7684\u6587\u6863\u3002 \u4e00\u65e6\u5b89\u88c5\u5b8c\u6210\uff0c\u6211\u4eec\u5c06\u6253\u5f00\u4e00\u4e2a\u65b0\u7684 Jupyter Notebook<\/a> \u5e76\u5bfc\u5165 MySQL Connector \u548c pandas\u3002<\/p>\n
import mysql.connector\nimport pandas as pd<\/pre>\n
\u4e3a\u4e86\u4ece\u6211\u4eec\u7684\u6570\u636e\u5e93\u4e2d\u8bfb\u53d6\u6570\u636e\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u8fde\u63a5\u5668<\/a>\u3002 \u8fd9\u662f\u901a\u8fc7 connect<\/code> \u65b9\u6cd5\u5b8c\u6210\u7684\uff0c\u6211\u4eec\u5411\u8be5\u65b9\u6cd5\u4f20\u9012\u8bbf\u95ee\u6570\u636e\u5e93\u6240\u9700\u7684\u51ed\u636e\uff1ahost<\/code>\u3001database<\/code> \u540d\u79f0\u3001user<\/code> \u548c password<\/code>\u3002 \u8fd9\u4e9b\u662f\u6211\u4eec\u5728\u4e0a\u4e00\u8282\u4e2d\u4f7f\u7528 Database<\/em>\uff08\u6570\u636e\u5e93\uff09\u5de5\u5177\u7a97\u53e3\u8bbf\u95ee\u6570\u636e\u5e93\u65f6\u4f7f\u7528\u7684\u76f8\u540c\u51ed\u636e\u3002<\/p>\n
mysql_db_connector = mysql.connector.connect(\n   host=\"localhost\",\n   database=\"demo\",\n   user=\"pycharm\",\n   password=\"pycharm\"\n)<\/pre>\n
\u6211\u4eec\u73b0\u5728\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u6e38\u6807<\/a>\u3002 \u8fd9\u5c06\u7528\u4e8e\u6267\u884c\u6211\u4eec\u5bf9\u6570\u636e\u5e93\u7684 SQL \u67e5\u8be2\uff0c\u5b83\u4f7f\u7528\u5728\u6211\u4eec\u7684\u8fde\u63a5\u5668\u4e2d\u5b58\u50a8\u7684\u51ed\u636e\u6765\u83b7\u5f97\u8bbf\u95ee\u6743\u9650\u3002<\/p>\n
mysql_db_cursor = mysql_db_connector.cursor()<\/pre>\n
\u6211\u4eec\u73b0\u5728\u53ef\u4ee5\u6267\u884c\u67e5\u8be2\u4e86\u3002 \u6211\u4eec\u4f7f\u7528\u6e38\u6807\u4e2d\u7684 execute<\/code> \u65b9\u6cd5<\/a>\u6765\u6267\u884c\u67e5\u8be2\uff0c\u5e76\u5c06\u67e5\u8be2\u4f5c\u4e3a\u5b9e\u53c2\u4f20\u9012\u3002<\/p>\n
delays_query = \"\"\"\n               SELECT AirportCode,\n                      SUM(FlightsDelayed) AS TotalDelayed\n                   FROM airlines\n                   WHERE TimeYear = 2016\n                   GROUP BY AirportCode\n                   HAVING SUM(FlightsDelayed) > 500;\n              \"\"\"\n\nmysql_db_cursor.execute(delays_query)<\/pre>\n
\u7136\u540e\uff0c\u6211\u4eec\u4f7f\u7528\u6e38\u6807\u7684 fetchall<\/code> \u65b9\u6cd5<\/a>\u68c0\u7d22\u7ed3\u679c\u3002<\/p>\n
mysql_delays_list = mysql_db_cursor.fetchall()<\/pre>\n
\u4e0d\u8fc7\uff0c\u5728\u8fd9\u4e00\u70b9\u4e0a\u6211\u4eec\u6709\u4e00\u4e2a\u95ee\u9898\uff1afetchall<\/code> \u4ee5\u5217\u8868\u5f62\u5f0f\u8fd4\u56de\u6570\u636e\u3002 \u4e3a\u4e86\u5c06\u5176\u8bfb\u5165 pandas\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u5176\u4f20\u5165\u4e00\u4e2a DataFrame\uff0c\u4f46\u662f\u6211\u4eec\u4f1a\u5931\u53bb\u5217\u540d\u3002\u5982\u679c\u6211\u4eec\u60f3\u8981\u521b\u5efa DataFrame\uff0c\u5219\u9700\u8981\u624b\u52a8\u6307\u5b9a\u5217\u540d\u3002<\/p>\n
\n
\"\"<\/figure>\n<\/div>\n
\u5e78\u597d\uff0cpandas \u63d0\u4f9b\u4e86\u4e00\u79cd\u66f4\u597d\u7684\u65b9\u5f0f\u3002 \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 read_sql<\/code> \u65b9\u6cd5<\/a>\uff0c\u4e00\u6b65\u5c06\u6211\u4eec\u7684\u67e5\u8be2\u8bfb\u5165\u4e00\u4e2a DataFrame\uff0c\u4e0d\u9700\u8981\u521b\u5efa\u6e38\u6807\u3002<\/p>\n
mysql_delays_df2 = pd.read_sql(delays_query, con=mysql_db_connector)<\/pre>\n
\u6211\u4eec\u53ea\u9700\u8981\u5c06\u6211\u4eec\u7684\u67e5\u8be2\u548c\u8fde\u63a5\u5668\u4f5c\u4e3a\u5b9e\u53c2\u4f20\u9012\uff0c\u4ee5\u4fbf\u4ece MySQL \u6570\u636e\u5e93\u4e2d\u8bfb\u53d6\u6570\u636e\u3002 \u67e5\u770b\u6211\u4eec\u7684 DataFrame\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6211\u4eec\u83b7\u5f97\u4e86\u4e0e\u4e0a\u9762\u5b8c\u5168\u4e00\u6837\u7684\u7ed3\u679c\uff0c\u4f46\u8fd9\u6b21\u5217\u540d\u88ab\u4fdd\u7559\u4e86\u3002<\/p>\n
\n
\"\"<\/figure>\n<\/div>\n
\u60a8\u53ef\u80fd\u5df2\u7ecf\u6ce8\u610f\u5230 PyCharm \u6709\u4e00\u4e2a\u4e0d\u9519\u7684\u529f\u80fd\uff0c\u8fd9\u4e2a\u529f\u80fd\u53ef\u4ee5\u5c06\u8bed\u6cd5\u9ad8\u4eae\u663e\u793a\u5e94\u7528\u4e8e SQL \u67e5\u8be2\uff0c\u5373\u4f7f\u67e5\u8be2\u5305\u542b\u5728 Python \u5b57\u7b26\u4e32\u4e2d\u4e5f\u53ef\u4ee5\u3002 \u7a0d\u540e\u5728\u672c\u535a\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u4ecb\u7ecd PyCharm \u5141\u8bb8\u60a8\u4f7f\u7528 SQL \u7684\u53e6\u4e00\u79cd\u65b9\u5f0f\u3002<\/p>\n
\u4f7f\u7528 SQLAlchemy \u8bfb\u5165\u6570\u636e<\/h2>\n
\u4f7f\u7528 MySQL Connector \u7684\u53e6\u4e00\u79cd\u65b9\u5f0f\u662f\u4f7f\u7528\u4e00\u4e2a\u53eb\u4f5c SQLAlchemy<\/a> \u7684\u8f6f\u4ef6\u5305\u3002 \u8fd9\u4e2a\u8f6f\u4ef6\u5305\u63d0\u4f9b\u4e86\u4e00\u79cd\u4e00\u7ad9\u5f0f\u65b9\u5f0f\uff0c\u53ef\u4ee5\u8fde\u63a5\u5230\u5305\u62ec MySQL \u5728\u5185\u7684\u4e00\u7cfb\u5217\u4e0d\u540c\u7684\u6570\u636e\u5e93\u3002 \u4f7f\u7528 SQLAlchemy \u7684\u4e00\u4e2a\u597d\u5904\u662f\uff0c\u67e5\u8be2\u4e0d\u540c\u6570\u636e\u5e93\u7c7b\u578b\u7684\u8bed\u6cd5\u5728\u4e0d\u540c\u7684\u6570\u636e\u5e93\u7c7b\u578b\u4e2d\u4fdd\u6301\u4e00\u81f4\u3002\u5982\u679c\u60a8\u6b63\u5728\u4f7f\u7528\u5927\u91cf\u4e0d\u540c\u7684\u6570\u636e\u5e93\uff0c\u65e0\u9700\u8bb0\u4f4f\u4e00\u5806\u4e0d\u540c\u7684\u547d\u4ee4\u3002<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u4ece PyPI<\/a> \u6216 Anaconda<\/a> \u5b89\u88c5 SQLAlchemy\u3002 \u7136\u540e\uff0c\u6211\u4eec\u5bfc\u5165 create_engine<\/code> \u65b9\u6cd5\uff0c\u5f53\u7136\u8fd8\u6709 pandas\u3002<\/p>\n
import pandas as pd\nfrom sqlalchemy import create_engine<\/pre>\n
\u6211\u4eec\u73b0\u5728\u9700\u8981\u521b\u5efa\u6211\u4eec\u7684\u5f15\u64ce<\/a>\u3002 \u8fd9\u4e2a\u5f15\u64ce\u5141\u8bb8\u6211\u4eec\u544a\u77e5 pandas \u6211\u4eec\u4f7f\u7528\u7684\u662f\u54ea\u79cd SQL \u65b9\u8a00\uff08\u5728\u6211\u4eec\u7684\u793a\u4f8b\u4e2d\u4e3a MySQL\uff09\uff0c\u5e76\u5411\u5176\u63d0\u4f9b\u8bbf\u95ee\u6211\u4eec\u7684\u6570\u636e\u5e93\u6240\u9700\u7684\u51ed\u636e\u3002 \u8fd9\u4e9b\u90fd\u4ee5\u4e00\u4e2a\u5b57\u7b26\u4e32\u7684\u5f62\u5f0f\u4f20\u9012\uff0c\u5373 [dialect]:\/\/[user]:[password]@[host]\/[database]<\/code>\u3002 \u6211\u4eec\u6765\u770b\u770b\u8fd9\u5bf9\u6211\u4eec\u7684 MySQL \u6570\u636e\u5e93\u6765\u8bf4\u662f\u4ec0\u4e48\u6837\u5b50\uff1a<\/p>\n
mysql_engine = create_engine(\"mysql+mysqlconnector:\/\/pycharm:pycharm@localhost\/demo\")<\/pre>\n
\u521b\u5efa\u5f15\u64ce\u540e\uff0c\u6211\u4eec\u53ea\u9700\u8981\u518d\u6b21\u4f7f\u7528 read_sql<\/code>\uff0c\u8fd9\u6b21\u5c06\u5f15\u64ce\u4f20\u9012\u7ed9 con<\/code> \u5b9e\u53c2\uff1a<\/p>\n
mysql_delays_df3 = pd.read_sql(delays_query, con=mysql_engine)<\/pre>\n
\u5982\u60a8\u6240\u89c1\uff0c\u6211\u4eec\u5f97\u5230\u7684\u7ed3\u679c\u4e0e\u5728 MySQL Connector \u4e2d\u4f7f\u7528 read_sql<\/code> \u65f6\u76f8\u540c\u3002<\/p>\n
\n
\"\"<\/figure>\n<\/div>\n
\u4f7f\u7528\u6570\u636e\u5e93\u7684\u9ad8\u7ea7\u9009\u9879<\/h2>\n
\u73b0\u5728\uff0c\u8fd9\u4e9b\u8fde\u63a5\u5668\u65b9\u6cd5\u5bf9\u4e8e\u63d0\u53d6\u6211\u4eec\u5df2\u7ecf\u77e5\u9053\u81ea\u5df1\u60f3\u8981\u7684\u67e5\u8be2\u975e\u5e38\u597d\u7528\uff0c\u4f46\u662f\u5982\u679c\u6211\u4eec\u60f3\u5728\u8fd0\u884c\u5b8c\u6574\u67e5\u8be2\u4e4b\u524d\u9884\u89c8\u6211\u4eec\u7684\u6570\u636e\u4f1a\u662f\u4ec0\u4e48\u6837\u5b50\uff0c\u6216\u8005\u60f3\u77e5\u9053\u6574\u4e2a\u67e5\u8be2\u4f1a\u82b1\u591a\u957f\u65f6\u95f4\uff0c\u8be5\u600e\u4e48\u529e\uff1f \u53c8\u5230\u4e86 PyCharm \u5927\u663e\u8eab\u624b\u7684\u65f6\u5019\u4e86\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e00\u4e9b\u4f7f\u7528\u6570\u636e\u5e93\u7684\u9ad8\u7ea7\u529f\u80fd\u3002<\/p>\n
\u5982\u679c\u6211\u4eec\u56de\u5230 Database<\/em>\uff08\u6570\u636e\u5e93\uff09\u5de5\u5177\u7a97\u53e3\uff0c\u53f3\u952e\u70b9\u51fb\u6211\u4eec\u7684\u6570\u636e\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5728 New<\/em>\uff08\u65b0\u5efa\uff09\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a Query Console<\/em>\uff08\u67e5\u8be2\u63a7\u5236\u53f0\uff09\u3002<\/p>\n
\n
\"\"<\/figure>\n<\/div>\n
\u8fd9\u5141\u8bb8\u6211\u4eec\u6253\u5f00\u4e00\u4e2a\u63a7\u5236\u53f0\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u5b83\u5728\u539f\u751f SQL \u4e2d\u5bf9\u6570\u636e\u5e93\u8fdb\u884c\u67e5\u8be2\u3002 \u63a7\u5236\u53f0\u7a97\u53e3\u5305\u62ec SQL \u4ee3\u7801\u8865\u5168\u548c\u5185\u7701\uff0c\u8fd9\u6837\uff0c\u60a8\u53ef\u4ee5\u66f4\u8f7b\u677e\u5730\u521b\u5efa\u67e5\u8be2\uff0c\u7136\u540e\u518d\u5c06\u67e5\u8be2\u4f20\u9012\u7ed9 Python \u4e2d\u7684\u8fde\u63a5\u5668\u8f6f\u4ef6\u5305\u3002<\/p>\n
\u9ad8\u4eae\u663e\u793a\u60a8\u7684\u67e5\u8be2\uff0c\u5e76\u70b9\u51fb\u5de6\u4e0a\u89d2\u7684 Execute<\/em>\uff08\u6267\u884c\uff09\u6309\u94ae\u3002<\/p>\n
\n
\"\"<\/figure>\n<\/div>\n
\u8fd9\u5c06\u5728 Services<\/em>\uff08\u670d\u52a1\uff09\u9009\u9879\u5361\u4e2d\u68c0\u7d22\u6211\u4eec\u67e5\u8be2\u7684\u7ed3\u679c\uff0c\u60a8\u53ef\u4ee5\u4ece\u8fd9\u4e2a\u9009\u9879\u5361\u68c0\u67e5\u6216\u5bfc\u51fa<\/a>\u67e5\u8be2\u3002 \u5bf9\u63a7\u5236\u53f0\u8fd0\u884c\u67e5\u8be2\u7684\u4e00\u4e2a\u597d\u5904\u662f\uff0c\u6700\u521d\u53ea\u4ece\u6570\u636e\u5e93\u4e2d\u68c0\u7d22\u524d 500 \u884c\uff0c\u8fd9\u610f\u5473\u7740\u60a8\u53ef\u4ee5\u4e86\u89e3\u5927\u578b\u67e5\u8be2\u7684\u7ed3\u679c\uff0c\u800c\u4e0d\u5fc5\u63d0\u53d6\u6240\u6709\u6570\u636e\u3002 \u60a8\u53ef\u4ee5\u8c03\u6574\u68c0\u7d22\u7684\u884c\u6570\uff1a\u8f6c\u5230 Settings\/Preferences | Tools | Database | Data Editor and Viewer<\/em>\uff08\u8bbe\u7f6e\/\u504f\u597d\u8bbe\u7f6e | \u5de5\u5177 | \u6570\u636e\u5e93 | \u6570\u636e\u7f16\u8f91\u548c\u67e5\u770b\u5668\uff09\uff0c\u5e76\u66f4\u6539 Limit page size to:<\/em>\uff08\u5c06\u9875\u9762\u5927\u5c0f\u9650\u5236\u4e3a:\uff09\u4e0b\u7684\u503c\u3002<\/p>\n
\n
\"\"<\/figure>\n<\/div>\n
\u8bf4\u5230\u5927\u578b\u67e5\u8be2\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u901a\u8fc7\u751f\u6210\u4e00\u4e2a\u6267\u884c\u8ba1\u5212<\/a>\u6765\u4e86\u89e3\u6211\u4eec\u7684\u67e5\u8be2\u5c06\u9700\u8981\u591a\u957f\u65f6\u95f4\u3002 \u5982\u679c\u6211\u4eec\u518d\u6b21\u9ad8\u4eae\u663e\u793a\u6211\u4eec\u7684\u67e5\u8be2\uff0c\u7136\u540e\u70b9\u51fb\u53f3\u952e\uff0c\u6211\u4eec\u53ef\u4ee5\u4ece\u83dc\u5355\u4e2d\u9009\u62e9 Explain Plan<\/em> | Explain Analyse<\/em>\u3002 \u8fd9\u5c06\u4e3a\u6211\u4eec\u7684\u67e5\u8be2\u751f\u6210\u4e00\u4e2a\u6267\u884c\u8ba1\u5212\uff0c\u663e\u793a\u67e5\u8be2\u8ba1\u5212\u5668\u4e3a\u68c0\u7d22\u7ed3\u679c\u6240\u91c7\u53d6\u7684\u6bcf\u4e00\u4e2a\u6b65\u9aa4\u3002 \u6267\u884c\u8ba1\u5212\u662f\u5b83\u4eec\u81ea\u5df1\u7684\u4e3b\u9898\uff0c\u6211\u4eec\u5e76\u4e0d\u9700\u8981\u4e86\u89e3\u6211\u4eec\u7684\u8ba1\u5212\u6240\u544a\u8bc9\u6211\u4eec\u7684\u4e00\u5207\u3002 \u4e0e\u6211\u4eec\u7684\u76ee\u7684\u6700\u76f8\u5173\u7684\u662f Actual Total Time<\/em>\uff08\u5b9e\u9645\u603b\u65f6\u95f4\uff09\u5217\uff0c\u5728\u8fd9\u91cc\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5728\u6bcf\u4e2a\u6b65\u9aa4\u4e2d\u8fd4\u56de\u6240\u6709\u884c\u9700\u8981\u591a\u957f\u65f6\u95f4\u3002 \u8fd9\u8ba9\u6211\u4eec\u80fd\u591f\u5bf9\u6574\u4e2a\u67e5\u8be2\u65f6\u95f4\u6709\u4e00\u4e2a\u5f88\u597d\u7684\u4f30\u8ba1\uff0c\u4ee5\u53ca\u9884\u5224\u6211\u4eec\u67e5\u8be2\u7684\u4efb\u4f55\u90e8\u5206\u662f\u5426\u53ef\u80fd\u7279\u522b\u8017\u65f6\u3002<\/p>\n
\n
\"\"<\/figure>\n<\/div>\n
\u60a8\u4e5f\u53ef\u4ee5\u901a\u8fc7\u70b9\u51fb Plan<\/em>\uff08\u8ba1\u5212\uff09\u9762\u677f\u5de6\u4fa7\u7684 Show Visualization<\/em>\uff08\u663e\u793a\u53ef\u89c6\u5316\uff09\u6309\u94ae\u6765\u53ef\u89c6\u5316\u6267\u884c\u3002<\/p>\n
\n
\"\"<\/figure>\n<\/div>\n
\u8fd9\u5c06\u5f39\u51fa\u4e00\u4e2a\u6d41\u7a0b\u56fe\uff0c\u8ba9\u60a8\u66f4\u8f7b\u677e\u6d4f\u89c8\u67e5\u8be2\u8ba1\u5212\u5668\u6b63\u5728\u8fdb\u884c\u7684\u6b65\u9aa4\u3002<\/p>\n
\n
\"\"<\/figure>\n<\/div>\n
\u5c06\u6570\u636e\u4ece MySQL \u6570\u636e\u5e93\u4e2d\u8bfb\u5165 pandas DataFrames \u975e\u5e38\u7b80\u5355\u3002PyCharm \u63d0\u4f9b\u4e86\u5f88\u591a\u5f3a\u5927\u7684\u5de5\u5177\u6765\u8ba9\u4f7f\u7528 MySQL \u6570\u636e\u5e93\u53d8\u5f97\u66f4\u7b80\u5355\u3002 \u5728\u4e0b\u4e00\u7bc7\u535a\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u4e86\u89e3\u5982\u4f55\u4f7f\u7528 PyCharm \u5c06\u6570\u636e\u4ece\u53e6\u4e00\u79cd\u6d41\u884c\u7684\u6570\u636e\u5e93\u7c7b\u578b \u2013 PostgreSQL \u6570\u636e\u5e93 \u2013 \u8bfb\u5165 pandas\u3002<\/p>\n
<\/p>\n
\u672c\u535a\u6587\u82f1\u6587\u539f\u4f5c\u8005\uff1a<\/p>\n\n    
\n        
\n            
\n                
\n                    \"\"\n                <\/div>\n                
\n                                                        <\/div>\n            <\/div>\n        <\/div>\n    <\/div>\n","protected":false},"author":1297,"featured_media":320796,"comment_status":"closed","ping_status":"closed","template":"","categories":[6943,952,1401,5108,2347],"tags":[2934,731,7057,1176,566],"cross-post-tag":[],"acf":[],"_links":{"self":[{"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/pycharm\/324541"}],"collection":[{"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/pycharm"}],"about":[{"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/types\/pycharm"}],"author":[{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/users\/1297"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/comments?post=324541"}],"version-history":[{"count":4,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/pycharm\/324541\/revisions"}],"predecessor-version":[{"id":324545,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/pycharm\/324541\/revisions\/324545"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/media\/320796"}],"wp:attachment":[{"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/media?parent=324541"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/categories?post=324541"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/tags?post=324541"},{"taxonomy":"cross-post-tag","embeddable":true,"href":"https:\/\/blog.jetbrains.com\/zh-hans\/wp-json\/wp\/v2\/cross-post-tag?post=324541"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}